The present invention relates to knowledge computing systems, and more particularly relates to large-scale distributed knowledge systems.
Organizations managing complex operations such as military command and control, medical diagnosis and treatment, and enterprise management have turned to large-scale knowledge systems as a means to effectively capture and maintain their knowledge assets. Those assets are being deployed across distributed computing resources including intranets and the Internet.
The technology supporting such systems has progressed to the point of being able to capture and use knowledge on the order of thousands of knowledge units (algorithms/rules/frames/axioms) to support human-like problem solving. However, until now there has not existed a knowledge management system able to support dynamic distributed problem solving over multiple large-scale and distributed knowledge systems. Such a system must be capable of manipulating and coordinating hundreds of thousands to millions of knowledge units.
One reason for that shortcoming is that typical systems, such as knowledge-based systems, employ inference engines that operate only on knowledge resident in specific knowledge bases that they control directly. For example, database search engines have the capability to manage information finding and retrieval, but they are not capable of retrieving knowledge in general form or combining knowledge retrieved from multiple sources into integrated inference patterns. In contrast, humans perform inference processes that involve widely distributed knowledge of more than one representation type or domain.
In addition, existing systems for performing distributed problem-solving functions typically require human reasoning intervention to bridge gaps in inference sequences, particularly between domain-specific and type-specific automated knowledge processing subsequences. In cases where the human intervention occurs, it usually takes the form of case-specific procedures implemented in software or hardware interfaces.
One example of an existing knowledge processing system is described in U.S. Pat. No. 5,465,319 issued to Ahamed, assigned to ATandT Corp., and entitled xe2x80x9cArchitecture for a Computer System Used for Processing Knowledge.xe2x80x9d Ahamed describes a knowledge machine for processing information and evolving knowledge. However, that system is limited because the knowledge stored must be organized in fixed links and in predetermined hierarchies, prior to any query of the knowledge. Moreover, the system described by Ahamed does not incorporate distributed inferencing that combines results of multiples of distinct, as well as overlapping and conflicting knowledge modules. In addition, the system of Ahamed requires centralized control of the allocation of knowledge processing hardware resources and of the execution of the knowledge representation of those hardware resources.
Another example of an existing knowledge processing system is described in U.S. Pat. No. 5,628,011 issued to Ahamed et al., assigned to ATandT Corp., and entitled xe2x80x9cNetwork-Based Intelligent Information-Sourcing Arrangement.xe2x80x9d Ahamed et al. describe a system in which knowledge bases are arranged hierarchically in a xe2x80x9cknowledge ring.xe2x80x9d Queries are collected and passed to the knowledge ring. The system restates the question in a way that the knowledge base is integrated, and, upon confirmation, responds to the query. However, that system is limited to the use of static knowledge trees. Any query must match an existing entry in the knowledge dictionary. The system of Ahamed et al. basically locates and retrieves pre-existing data by a search of the static knowledge tree, which is essentially a taxonomy of hierarchically-related subject names.
Yet another example of an existing knowledge processing system is described in U.S. Pat. No. 5,809,493 issued to Ahamed et al., assigned to Lucent Technologies, Inc. entitled xe2x80x9cKnowledge Processing System Employing Confidence Levels.xe2x80x9d Ahamed et al. describe a knowledge processing system which processes knowledge in the knowledge domain to generate incremental and integrated conclusions, and in the numerical domain to generate confidence levels. The knowledge processing system iteratively revises solutions to generate an optimal solution based on the confidence levels. However, that system fails to describe dynamic problem solving, but rather builds confidence in pre-existing problem solutions by building databases of previous attempts to solve the same problem. The described system uses pre-determined (non-dynamic) knowledge operations mappings.
Accordingly, a need exists for a knowledge management system for dynamic, distributed problem-solving systems.
The present invention overcomes the problems identified above by providing a knowledge management system that supports inquiries of distributed knowledge resources. Those inquiries may be in the form of questions or problem statements presented by a user. Interaction between a user and the knowledge resources is mediated by a collection of cooperative intelligent agents.
The cooperative intelligent agents incorporate generalized automated negotiation and distributed inference (i.e., problem-solving) processes. Using those processes in a hierarchical architecture, the invention analyzes input problem statements and organizes the problem statements as sets of tasks. In pursuit of each task, the invention solicits accessible knowledge repositories, represented by knowledge agents, for relevant knowledge, and then analyzes and integrates responses from those knowledge repositories. The invention may then provide the responses to a human user or a using process.
The invention adaptively and dynamically synthesizes problem-specific knowledge interfaces and reasoning procedures as the problem-solving process moves forward. The invention extends automated inference capability to make use of a large number of knowledge sources of different types, in different locations, and covering different domains of expertise.
In one aspect, the invention integrates established knowledge-based environments or other software-based knowledge by breaking them down into well-defined, independent xe2x80x9cknowledge modules.xe2x80x9d The knowledge modules contain knowledge and knowledge processing capabilities (e.g., inferencing, database management, algorithms, etc.) for a domain of knowledge. Knowledge modules are independent and may contain overlapping or exclusive knowledge. Each knowledge module is combined with a cooperative intelligent agent to form a unit that facilitates integration with other units. The units allow the system to support cooperative reasoning through distributed inference (and problem-solving) processing. The encapsulation of restricted knowledge content within the units and their well-defined interfaces aids verification. The resulting architecture supports the development of a continuously evolving, distributed knowledge system.
More specifically, the present invention provides a hierarchical knowledge management system having three general layers: a user interface layer, a meta agent layer, and a knowledge agent layer. Each layer in the system includes one or more intelligent agents responsible for one portion of the distributed problem-solving inferencing process. The user interface layer mediates the direct interactions with the user, which affords control of and a window into the system""s activities. The meta agent layer analyzes user queries or problem formulations from the user interface layer, allocates tasks to the knowledge agent layer, resolves conflicts arising from the knowledge agent layer, and consolidates (including fusing and deconflicting) results provided by the knowledge agent layer. The knowledge agent layer provides an interaction mechanism for knowledge modules having associated knowledge agents within the knowledge agent layer. Each agent in the system includes inter-agent abstract communications facilities with the capability to negotiate with each other, conduct joint planning, and to collaborate in the execution of planned tasks.
In addition to the three layers just mentioned, an agent service layer provides services for maintaining a registry of agents in the system, as well as supporting the distributed problem solving. The registry identifies each agent""s capabilities and interests, and contains knowledge about the relationships between them. The meta agent layer and the knowledge agent layer may confer with the agent service layer to identify those other resources capable of furthering the problem-solving process. A matchmaking facility is provided for notifying agents interested in a capability of other agents that provide the capability.
One advantage of the present invention over existing technologies is that inferencing is distributed and cooperative over a distributed environment. In other words, the problem-solving process has been removed from a centrally-located reasoning mechanism and made granular. Rather than relying on a single knowledge-based system to formulate and execute a problem-solving process, inferencing mechanisms are distributed to many, smaller knowledge systems with each having a more clearly defined set of interests and products. Each smaller knowledge system is provided with knowledge processing capabilities for its domain of knowledge. A meta agent is responsible for decomposing a general inquiry into a series of constituent tasks. Each task is formulated based on knowledge of the capabilities of the underlying knowledge systems. By cooperating with each other, the meta agent and knowledge agents at each knowledge system accomplish each task toward solving the global problem.
Another advantage is that a knowledge management system in accordance with the present invention is scalable and modular so it can evolve continuously without a negative impact on previously existing components. Once established knowledge systems are broken down into smaller units with distributed inferencing capability, each of those smaller units may be modified, added, deleted, replaced, or supplemented with additional units having greater processing, knowledge, or inferencing capabilities. This advantage allows the system to evolve as technology develops and knowledge systems become more and more complex.
Yet another advantage is that a knowledge management system in accordance with the present invention is non-monolithic. The distributed nature of the disclosed knowledge management system prevents a single inference engine from dominating the system. Problem-solving processes are conducted in a cooperative manner among many intelligent agents, each having its own realm of expertise.